Molecular sensors are high sensitivity probes capable of reporting quantitative biological information at the molecular and cellular levels. In particular, imaging genetically encoded sensors based on fluorescent and luminescent proteins, which can be permanently introduced into cells and organisms, can yield detailed information on numerous cellular processes such as signal transduction and gene regulation. Such sensors have enormous potential for providing data to aid in the investigation of complex biological mechanisms and disease pathologies. Before their full potential can be realized, however, sensor properties must be improved, and model systems must be constructed to test and highlight their utility. Molecular sensors will be developed to study an important model system with medical potential, adult neural stem cells. This system will be employed to emphasize the idea that the complex gene regulation and signal transduction mechanisms that translate extracellular signals into intracellular decisions can be elucidated only by taking measurements at multiple junctures in these processes, in real time and at the single cell level. Firefly luciferase reporters will be developed to quantify the regulation of key transcription factors involved in two neural stem cell fate decisions, self-renewal and astrocytic differentiation, controlled by Shh and Notch signaling. Simultaneously, fluorescent protein sensors will report stem cell fate commitment. Sensor function will be validated using immunofluorescence and quantitative PCR methods. Finally, to further improve the properties of firefly luciferase reporters, a high throughput directed evolution approach based on DNA shuffling will be employed to create novel luciferase variants with higher sensitivity. Our Specific Aims are: 1) To employ molecular sensors to determine at the single cell level whether threshold levels of extracellular signals switch stem cell fate decisions 2) To determine whether higher sensitivity variants of luciferase can be created using a directed evolution approach.